信息融合过程中证据冲突研究
摘要
信息融合应用中对冲突证据的有效处理一直是研究的热点,本文主要对多传感器信息融合过程中证据间的冲突进行了理论分析,并对开放世界里基于TBM模型的融合算法进行了研究,具体内容如下:
(1)对证据冲突产生的原因进行了深入分析,讨论了衡量冲突的表征因子,使用矛盾因子和赌博信度距离共同对冲突进行有效的定义和判断,引入了基于证据距离的证据一致性参数对证据进行修正,以保证融合的有效性并应用于故障诊断中。
(2)提出了针对数值属性获取一般信度指派值的方法。本方法首先构建了各个目标属性的三角模糊函数值和权重值,进而根据这些值获得目标属于各个类别的可能性,并通过归一化生成传感器报告的一般信度指派值。仿真结果证明该方法能够有效地应用于开放世界的信息融合。
(3)基于TBM模型提出了适用于开放世界的融合框架和决策方法。在表示层上对获取的信度值进行有效性判断及修正,降低了冲突信度对融合结果的影响,实现了冲突情况下信度的合成;在决策层上通过pignistic转换得到辨识框架中每个命题的概率分布,并采用基于规则的方法给出最终的识别结果。
The study of effective combination rules in DS theory when evidences are in conflict is always an interesting topic. This research is concentrated on analyzing the degree of conflict among evidences in Multi-Sensor Information Fusion System and proposing the combination rules based on the TBM model. The major works are as follows:
(1) Discussed what really constitutes a conflict among two beliefs, and proposed the new judgment of the conflict by using both the mass of the combined belief assigned to the empty set before normalization and the distance between betting commitments of beliefs. After analyzing the different fusion conditions defined by the two values, a set of rules guiding whether Dempster’s rule can be used or cannot be applied are proposed.
(2) Proposed the method of computing the general basic belief assignment used in the object detection in the open world. First, the triangle fuzzy number and the weight of all the properties are constructed based on the original data, then the general bbas of sensor reports which can be used in the information fusion of the open world are computed according to the triangle fuzzy numbers and their weights.
(3) Proposed the fusion model and the decision rules of the open world based on the TBM model. According to the belief assigned to the empty set of a bba and the value of the conflict among evidences, the general bbas obtained from sensor reports are abandoned or combined or updated on the fusion center of the credal level, and then translated into probability of each object by using the pignistic transformation when needed. The decisions are made using the pignistic probabilities derived from the bba by the pignistic transformation.
(1)对证据冲突产生的原因进行了深入分析,讨论了衡量冲突的表征因子,使用矛盾因子和赌博信度距离共同对冲突进行有效的定义和判断,引入了基于证据距离的证据一致性参数对证据进行修正,以保证融合的有效性并应用于故障诊断中。
(2)提出了针对数值属性获取一般信度指派值的方法。本方法首先构建了各个目标属性的三角模糊函数值和权重值,进而根据这些值获得目标属于各个类别的可能性,并通过归一化生成传感器报告的一般信度指派值。仿真结果证明该方法能够有效地应用于开放世界的信息融合。
(3)基于TBM模型提出了适用于开放世界的融合框架和决策方法。在表示层上对获取的信度值进行有效性判断及修正,降低了冲突信度对融合结果的影响,实现了冲突情况下信度的合成;在决策层上通过pignistic转换得到辨识框架中每个命题的概率分布,并采用基于规则的方法给出最终的识别结果。
The study of effective combination rules in DS theory when evidences are in conflict is always an interesting topic. This research is concentrated on analyzing the degree of conflict among evidences in Multi-Sensor Information Fusion System and proposing the combination rules based on the TBM model. The major works are as follows:
(1) Discussed what really constitutes a conflict among two beliefs, and proposed the new judgment of the conflict by using both the mass of the combined belief assigned to the empty set before normalization and the distance between betting commitments of beliefs. After analyzing the different fusion conditions defined by the two values, a set of rules guiding whether Dempster’s rule can be used or cannot be applied are proposed.
(2) Proposed the method of computing the general basic belief assignment used in the object detection in the open world. First, the triangle fuzzy number and the weight of all the properties are constructed based on the original data, then the general bbas of sensor reports which can be used in the information fusion of the open world are computed according to the triangle fuzzy numbers and their weights.
(3) Proposed the fusion model and the decision rules of the open world based on the TBM model. According to the belief assigned to the empty set of a bba and the value of the conflict among evidences, the general bbas obtained from sensor reports are abandoned or combined or updated on the fusion center of the credal level, and then translated into probability of each object by using the pignistic transformation when needed. The decisions are made using the pignistic probabilities derived from the bba by the pignistic transformation.
引文
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2 D.L. Hall, J. Llinas. An introduction to Multisensor Data Fusion. ProceedingsoftheIEEE. 1997: 6-23.
3 E. Waltz. Data Fusion for C3I. PaloAlto,CA:EWCommunications. 1986: 217-226.
4 W.S. Gesing, D.B. Reid. An integrated multisensor aircraft track recovery system for remote sensing. IEEETrans.Automat.Contc, Vol. 28, No. 3, 1983: 356-363
5 S.A. Shafer, A. Stentz, C.E. Thorpe. Architecture for sensory fusion in amobile robot. Proc IEEE Int. Conf. Robotics and Automat,SanFrancisco. 1986: 2002-2011.
6 D.J. Kriegman, l.E. Triend, T.O. Binfork. Stereo vision and navigation in buildings for mobile robots. IEEETrans.Robot.Automat, Vol. 5, No. 6, 1989: 792-803
7 C.C. Ruokangas. Dynamic control of robotic welding:On-going research. Proc.SPIE,Appl.ArtificialIntel.VIII. 1990: 332-343.
8 K. Demirbas. Integration of tactile sensors and machine vision for control of robotic manipulators. Robots 9 Conf. Proc. 1987: 37-71.
9 L. Valet, G.Mauris, P. Bolon. A Statistical Overview of Recent Literature in Information Fusion. Fusion 2000, 3`0 International Conference on Information Fusion. 2000.
10 E. Waltz, J. Llinas. Multisensor Data Fusion. Artech House, Boston. 1990.
11 权太范. 信息融合神经网络-模糊推理理论与应用. 北京: 国防工业出版社. 2002.
12 刘同明, 夏祖勋, 解洪成. 数据融合技术及其应用. 国防工业出版社. 1998.
13 郁文贤, 雍少为, 郭桂蓉. 多传感器信息融合技术评述. 国防科技大学学报, Vol. 16, No. 3, 1994: 1-1
14 L.A. Llein. Sensor and data fusion concepts and applications. SPIE Optical Engineering Press, 1999: 47-176
15 翟翌立, 戴逸松. 多传感器数据自适应加权数据融合估计算法的研究. 计量学报, Vol. 19, No. 1, 1998: 69-75
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17 K. Chou, A. Willsky, A. Benveniste. Multiscale recursive estimation,datafusion and regularization. IEEE Trans. on Automatic Control, Vol. 39, No. 4, 1994: 464-478
18 Q. Gan, C.J. Harris. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Transactions on Aerospace and Electronic Systems, Vol. 37, No. 1, 2001: 273-27
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21 R.S. Rao, H. Durrant-Whyte. A Decentralized Bayesian Algorithm for Identification of TrackedT argets. IEEE Trans. on SMC, Vol. 23, No. 6, 1993: 1683-1698
22 B. Ristic, S. Ph. Recursive Classification of Multiple Objects Using Discordant and Non-Specific Data. sumitted, 2004:
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25 A.P. Dempster. Upper and lower probability inferences based on a sample from a finite univariate population. Biometrika, Vol. 54, 1967: 515--528
26 A.P. Dempster. A generalization of Bayesian inference. J. Roy. Statist. Soc. B., Vol. 30, 1968: 205--247
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30 P. Smets. What is Dempster-Shafer's model? Advances in the Dempster-Shafer Theory of Evidence. 1994: 5--34.
31 J. Dezert. Foundations for a new theory of plausible and paradoxical reasoning. Information and Security, Vol. 9, 2002: 13--57
32 F. Voorbraak. On the justification of Dempster's rule of combination. Artificial Intelligence, Vol. 48, 1991: 171--197
33 滕召胜, 罗隆福, 童调生. 智能检测系统与数据融合. 机械工业出版社. 1999.
34 L.A. Zadeh, On the validity of Dempster's rule of combination of evidence. 1979, University of California, Berkely.
35 L.A. Zadeh. Fuzzy sets. Information and Control, Vol. 8, 1965: 338-353
36 L.A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Information Science, Vol. 8-9, No. Part 1, 2 and 3, 1975: 199-249 301-357
37 H.J. Zimmermann. Fuzzy Set Theory And Its Applications. Kiuwer Academic Publishers. 2001.
38 A. Kauffman, M.M. Gupta. Introduction to Fuzzy Arithmetic: Theory and Applications. Van Nostrand Reinhold. 1985.
39 H. Prade, D. Dubois. Operations on fuzzy numbers. International Journal of Systems Science, Vol. 9, 1978: 613-626
40 W. Pedrycz, P.J.M. Laarhoven. A fuzzy extension of Satty's priority theory. Fuzzy Sets and Systems, Vol. 11, 1983: 229-241
41 A.P. Dempster. Upper and lower probabilities generated by a random closed interval. Ann. Math. Statistics, Vol. 39, 1968: 957--966
42 G. Shafer. Perspectives in the theory and practice of belief functions. Int. J. Approximate Reasoning, Vol. 4, 1990: 323--362
43 H. Ruspini, J.D. Lawrence, T.M. Strat. Understanding evidential reasoning. Int. J. Approximate Reasoning, Vol. 6, 1992: 401--424
44 A. Josang. The Consensus Operator for Combining Beliefs. Artificial Intelligence Journal, Vol. 142, 2002: 157--170
45 J. Schubert. On Nonspecific Evidence. Int. J. Intell. Syst., Vol. 8, No. 6, 1993: 711--725
46 Y.G. Wu, J.Y. Yang, K. Lin, L.J. Liu. On the evidence inference theory.Information Science, Vol. 89, No. 3/4, 1996: 245-260
47 何友,王国宏. 多传感器信息融合及应用. 北京:电子工业出版社. 2000.
48 I. Bloch. Information combination operator for data fusion: a comparative review with classification. IEEE Trans. SMC, Vol. 26, 1996: 52--67
49 J.A. Barnett. Computational methods for a mathematical theory of evidence. Int. Joint Conf. on Artificial Intelligence. 1981: 868--875.
50 J. Gordon, E.H. Shortliffe. A method for managing evidential reasoning in a hierarchical hypothesis space. Artificial Intelligence, Vol. 26, 1985: 323--357
51 J. Pearl. Reasoning with Belief Functions: an Analysis of Compatibility. Int. J. Approximate Reasoning, Vol. 4, 1990: 363--390
52 G. Shafer, R.P. Srivastava. The Bayesian and Belief-Function formalisms---a general approach for auditing. Auditing---A Journal of Practice and Theory, Vol. 9, 1990: 110--137
53 F. Voorbraak. A computationally efficient approximation of Dempster-Shafer theory. Int. J. Man Machine Studies, Vol. 30, 1989: 525-536
54 F. Voorbraak. As Far as I Know: Epistemic Logic and Uncertainty. Dissertation, Utrecht University. 1993.
55 D. Dubois, H. Prade. Possibility theory : an approach to computerized processing of uncertainty. Plenum Press. 1988.
56 B. Tessem. Approximations for efficient computation in the theory of evidence. Artificial Intelligence, Vol. 61, 1993: 315--329
57 M.A. Simard, J. Couture, E. Bosse. Data fusion of multiple-sensors attribute information for target-identity estimation using a Dempster-Shafer evidential combination algorithm. SPIE Proc., Vol. 2759, 1996: 577--588
58 R.P.S. Mahler. Combining ambiguous evidence with respect to ambiguous a priori knowledge, II: Fuzzy Logic. Fuzzy Sets ans Systems, Vol. 75, 1995: 319--354
59 R.P.S. Mahler. Combining ambiguous evidence with respect to ambiguous a priori knowledge, I: Boolean Logic. IEEE-SMC, A, Vol. 26, 1996: 27--41
60 M. Ishizuka, K.S. Fu, J.T.P. Yao. Inference procedure with uncertainty for problem reduction method. Information Science, Vol. 28, 1982: 179--206
61 康耀红. 数据融合理论与应用. 西安电子科技大学出版社. 1997.
62 T. Denoeux. A k-nearest neighbor classification rule based on {Dempster-Shafer} theory. IEEE Transactions on Systems, Man and Cybernetics, Vol. 25, No. 5, 1995: 804--813
63 倪国强, 梁好臣. 基于 Dempster-Shafer 证据理论的数据融合技术研究. 北京理工大学学报, Vol. 21, No. 5, 2001: 603--609
64 T. Denux. An evidence-theoretic neural network classifier. IEEE International Conference on Systems, Man and Cybernetics. 1995: 712--717.
65 R.R. Yager. On the Dempster-Shafer Framework and New Combination Rules. Information Sciences, Vol. 41, 1987: 93--137
66 D. Dubois, H. Prade. Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence, Vol. 4, 1988: 244--264
67 S. Ph. Constructing the pignistic probability function in a context of uncertainty. Uncertainty in Artificial Intelligence 5. 1990: 29--40.
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2 D.L. Hall, J. Llinas. An introduction to Multisensor Data Fusion. ProceedingsoftheIEEE. 1997: 6-23.
3 E. Waltz. Data Fusion for C3I. PaloAlto,CA:EWCommunications. 1986: 217-226.
4 W.S. Gesing, D.B. Reid. An integrated multisensor aircraft track recovery system for remote sensing. IEEETrans.Automat.Contc, Vol. 28, No. 3, 1983: 356-363
5 S.A. Shafer, A. Stentz, C.E. Thorpe. Architecture for sensory fusion in amobile robot. Proc IEEE Int. Conf. Robotics and Automat,SanFrancisco. 1986: 2002-2011.
6 D.J. Kriegman, l.E. Triend, T.O. Binfork. Stereo vision and navigation in buildings for mobile robots. IEEETrans.Robot.Automat, Vol. 5, No. 6, 1989: 792-803
7 C.C. Ruokangas. Dynamic control of robotic welding:On-going research. Proc.SPIE,Appl.ArtificialIntel.VIII. 1990: 332-343.
8 K. Demirbas. Integration of tactile sensors and machine vision for control of robotic manipulators. Robots 9 Conf. Proc. 1987: 37-71.
9 L. Valet, G.Mauris, P. Bolon. A Statistical Overview of Recent Literature in Information Fusion. Fusion 2000, 3`0 International Conference on Information Fusion. 2000.
10 E. Waltz, J. Llinas. Multisensor Data Fusion. Artech House, Boston. 1990.
11 权太范. 信息融合神经网络-模糊推理理论与应用. 北京: 国防工业出版社. 2002.
12 刘同明, 夏祖勋, 解洪成. 数据融合技术及其应用. 国防工业出版社. 1998.
13 郁文贤, 雍少为, 郭桂蓉. 多传感器信息融合技术评述. 国防科技大学学报, Vol. 16, No. 3, 1994: 1-1
14 L.A. Llein. Sensor and data fusion concepts and applications. SPIE Optical Engineering Press, 1999: 47-176
15 翟翌立, 戴逸松. 多传感器数据自适应加权数据融合估计算法的研究. 计量学报, Vol. 19, No. 1, 1998: 69-75
16 L.A. Klein. A Boolean algebra approach to multiple sensor voting fusion. IEEE Trans Aerosp. Electron Syst., Vol. 29, 1993: 317--327
17 K. Chou, A. Willsky, A. Benveniste. Multiscale recursive estimation,datafusion and regularization. IEEE Trans. on Automatic Control, Vol. 39, No. 4, 1994: 464-478
18 Q. Gan, C.J. Harris. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Transactions on Aerospace and Electronic Systems, Vol. 37, No. 1, 2001: 273-27
19 W. Pedrycz. Identification in fuzzy systems. IEEE Transactions on Systems, Man and Cybernetics, Vol. 14, 1989: 361-366
20 L.R. Medsker, E. Turban. Integration Expert Systems and Neural Networks for Decision Support. Expert Systems with Applications, Vol. 794, 1994: 553-562
21 R.S. Rao, H. Durrant-Whyte. A Decentralized Bayesian Algorithm for Identification of TrackedT argets. IEEE Trans. on SMC, Vol. 23, No. 6, 1993: 1683-1698
22 B. Ristic, S. Ph. Recursive Classification of Multiple Objects Using Discordant and Non-Specific Data. sumitted, 2004:
23 J. Schubert. On rho in a decision-theoretic apparatus of Dempster-Shafer theory. Int. J. Approximate Reasoning, Vol. 13, No. 3, 1995: 185--200
24 A.P. Dempster. Upper and lower probabilities induced by a multiple valued mapping. Ann. Math. Statistics, Vol. 38, 1967: 325--339
25 A.P. Dempster. Upper and lower probability inferences based on a sample from a finite univariate population. Biometrika, Vol. 54, 1967: 515--528
26 A.P. Dempster. A generalization of Bayesian inference. J. Roy. Statist. Soc. B., Vol. 30, 1968: 205--247
27 G. Shafer. A Mathematical Theory of Evidence. Princeton Univ. Press. Princeton, NJ. 1976.
28 P. Smets. The transferable belief model and random sets. Int. J. Intell. Systems, Vol. 7, 1992: 37--46
29 P. Smets. The combination of evidence in the transferable belief model. IEEE Pattern Analysis and Machine Intelligence, Vol. 12, 1990: 447--458
30 P. Smets. What is Dempster-Shafer's model? Advances in the Dempster-Shafer Theory of Evidence. 1994: 5--34.
31 J. Dezert. Foundations for a new theory of plausible and paradoxical reasoning. Information and Security, Vol. 9, 2002: 13--57
32 F. Voorbraak. On the justification of Dempster's rule of combination. Artificial Intelligence, Vol. 48, 1991: 171--197
33 滕召胜, 罗隆福, 童调生. 智能检测系统与数据融合. 机械工业出版社. 1999.
34 L.A. Zadeh, On the validity of Dempster's rule of combination of evidence. 1979, University of California, Berkely.
35 L.A. Zadeh. Fuzzy sets. Information and Control, Vol. 8, 1965: 338-353
36 L.A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Information Science, Vol. 8-9, No. Part 1, 2 and 3, 1975: 199-249 301-357
37 H.J. Zimmermann. Fuzzy Set Theory And Its Applications. Kiuwer Academic Publishers. 2001.
38 A. Kauffman, M.M. Gupta. Introduction to Fuzzy Arithmetic: Theory and Applications. Van Nostrand Reinhold. 1985.
39 H. Prade, D. Dubois. Operations on fuzzy numbers. International Journal of Systems Science, Vol. 9, 1978: 613-626
40 W. Pedrycz, P.J.M. Laarhoven. A fuzzy extension of Satty's priority theory. Fuzzy Sets and Systems, Vol. 11, 1983: 229-241
41 A.P. Dempster. Upper and lower probabilities generated by a random closed interval. Ann. Math. Statistics, Vol. 39, 1968: 957--966
42 G. Shafer. Perspectives in the theory and practice of belief functions. Int. J. Approximate Reasoning, Vol. 4, 1990: 323--362
43 H. Ruspini, J.D. Lawrence, T.M. Strat. Understanding evidential reasoning. Int. J. Approximate Reasoning, Vol. 6, 1992: 401--424
44 A. Josang. The Consensus Operator for Combining Beliefs. Artificial Intelligence Journal, Vol. 142, 2002: 157--170
45 J. Schubert. On Nonspecific Evidence. Int. J. Intell. Syst., Vol. 8, No. 6, 1993: 711--725
46 Y.G. Wu, J.Y. Yang, K. Lin, L.J. Liu. On the evidence inference theory.Information Science, Vol. 89, No. 3/4, 1996: 245-260
47 何友,王国宏. 多传感器信息融合及应用. 北京:电子工业出版社. 2000.
48 I. Bloch. Information combination operator for data fusion: a comparative review with classification. IEEE Trans. SMC, Vol. 26, 1996: 52--67
49 J.A. Barnett. Computational methods for a mathematical theory of evidence. Int. Joint Conf. on Artificial Intelligence. 1981: 868--875.
50 J. Gordon, E.H. Shortliffe. A method for managing evidential reasoning in a hierarchical hypothesis space. Artificial Intelligence, Vol. 26, 1985: 323--357
51 J. Pearl. Reasoning with Belief Functions: an Analysis of Compatibility. Int. J. Approximate Reasoning, Vol. 4, 1990: 363--390
52 G. Shafer, R.P. Srivastava. The Bayesian and Belief-Function formalisms---a general approach for auditing. Auditing---A Journal of Practice and Theory, Vol. 9, 1990: 110--137
53 F. Voorbraak. A computationally efficient approximation of Dempster-Shafer theory. Int. J. Man Machine Studies, Vol. 30, 1989: 525-536
54 F. Voorbraak. As Far as I Know: Epistemic Logic and Uncertainty. Dissertation, Utrecht University. 1993.
55 D. Dubois, H. Prade. Possibility theory : an approach to computerized processing of uncertainty. Plenum Press. 1988.
56 B. Tessem. Approximations for efficient computation in the theory of evidence. Artificial Intelligence, Vol. 61, 1993: 315--329
57 M.A. Simard, J. Couture, E. Bosse. Data fusion of multiple-sensors attribute information for target-identity estimation using a Dempster-Shafer evidential combination algorithm. SPIE Proc., Vol. 2759, 1996: 577--588
58 R.P.S. Mahler. Combining ambiguous evidence with respect to ambiguous a priori knowledge, II: Fuzzy Logic. Fuzzy Sets ans Systems, Vol. 75, 1995: 319--354
59 R.P.S. Mahler. Combining ambiguous evidence with respect to ambiguous a priori knowledge, I: Boolean Logic. IEEE-SMC, A, Vol. 26, 1996: 27--41
60 M. Ishizuka, K.S. Fu, J.T.P. Yao. Inference procedure with uncertainty for problem reduction method. Information Science, Vol. 28, 1982: 179--206
61 康耀红. 数据融合理论与应用. 西安电子科技大学出版社. 1997.
62 T. Denoeux. A k-nearest neighbor classification rule based on {Dempster-Shafer} theory. IEEE Transactions on Systems, Man and Cybernetics, Vol. 25, No. 5, 1995: 804--813
63 倪国强, 梁好臣. 基于 Dempster-Shafer 证据理论的数据融合技术研究. 北京理工大学学报, Vol. 21, No. 5, 2001: 603--609
64 T. Denux. An evidence-theoretic neural network classifier. IEEE International Conference on Systems, Man and Cybernetics. 1995: 712--717.
65 R.R. Yager. On the Dempster-Shafer Framework and New Combination Rules. Information Sciences, Vol. 41, 1987: 93--137
66 D. Dubois, H. Prade. Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence, Vol. 4, 1988: 244--264
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